Beijing Qihoo Technology Co., Ltd. v. Tencent Technology (Shenzhen) Co., Ltd. and Shenzhen Tencent Computer System Co., Ltd. (Dispute over the Abuse of Market Dominant Position)—Analysis Methods and Ideas for the Definition of the Relevant Markets and the Abuse of Market Dominant Position in the Internet Environment

Abstract

Natural language processing models based on neural networks are vulnerable to adversarial examples. These adversarial examples are imperceptible to human readers but can mislead models to make the wrong predictions. In a black-box setting, attacker can fool the model without knowing model's parameters and architecture. Previous works on word-level attacks widely use single semantic space and greedy search as a search strategy. However, these methods fail to balance the attack success rate, quality of adversarial examples and time consumption. In this paper, we propose BeamAttack, a textual attack algorithm that makes use of mixed semantic spaces and improved beam search to craft high-quality adversarial examples. Extensive experiments demonstrate that BeamAttack can improve attack success rate while saving numerous queries and time, e.g., improving at most 7% attack success rate than greedy search when attacking the examples from MR dataset. Compared with heuristic search, BeamAttack can save at most 85% model queries and achieve a competitive attack success rate. The adversarial examples crafted by BeamAttack are highly transferable and can effectively improve model's robustness during adversarial training. Code is available at https://github.com/zhuhai-ustc/beamattack/tree/master

Layered Magnesium Phosphate as an Environmentally Friendly Solid Lubrication Additive: Morphology Control and Tribological Properties

“…The purpose of adding solid lubricating materials to lubricating grease is to improve its lubricating properties and increase the service life and high-temperature resistance of the matrix . The test temperatures for the tribological tests (Figures –) were 50 or 80 °C.…”